A Data Intrusion Tolerance Model Based on an Improved Evolutionary Game Theory for the Energy Internet

Malicious attacks against data are unavoidable in the interconnected,open and shared Energy Internet(EI),Intrusion tolerant techniques are critical to the data security of EI.Existing intrusion tolerant techniques suffered from problems such as low adaptability,policy lag,and difficulty in determining the degree of tolerance.To address these issues,we propose a novel adaptive intrusion tolerance model based on game theory that enjoys two-fold ideas:(1)it constructs an improved replica of the intrusion tolerance model of the dynamic equation evolution game to induce incentive weights;and (2)it combines a tournament competition model with incentive weights to obtain optimal strategies for each stage of the game process.Extensive experiments are conducted in the IEEE 39-bus system,whose results demonstrate the feasibility of the incentive weights,confirm the proposed strategy strengthens the system’s ability to tolerate aggression,and improves the dynamic adaptability and response efficiency of the aggression-tolerant system in the case of limited resources.

Network Defense Decision-Making Based on Deep Reinforcement Learning and Dynamic Game Theory

Existing researches on cyber attackdefense analysis have typically adopted stochastic game theory to model the problem for solutions,but the assumption of complete rationality is used in modeling,ignoring the information opacity in practical attack and defense scenarios,and the model and method lack accuracy.To such problem,we investigate network defense policy methods under finite rationality constraints and propose network defense policy selection algorithm based on deep reinforcement learning.Based on graph theoretical methods,we transform the decision-making problem into a path optimization problem,and use a compression method based on service node to map the network state.On this basis,we improve the A3C algorithm and design the DefenseA3C defense policy selection algorithm with online learning capability.The experimental results show that the model and method proposed in this paper can stably converge to a better network state after training,which is faster and more stable than the original A3C algorithm.Compared with the existing typical approaches,Defense-A3C is verified its advancement.

Computation Offloading in Edge Computing for Internet of Vehicles via Game Theory

With the rapid advancement of Internet of Vehicles(IoV)technology,the demands for real-time navigation,advanced driver-assistance systems(ADAS),vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications,and multimedia entertainment systems have made in-vehicle applications increasingly computingintensive and delay-sensitive.These applications require significant computing resources,which can overwhelm the limited computing capabilities of vehicle terminals despite advancements in computing hardware due to the complexity of tasks,energy consumption,and cost constraints.To address this issue in IoV-based edge computing,particularly in scenarios where available computing resources in vehicles are scarce,a multi-master and multi-slave double-layer game model is proposed,which is based on task offloading and pricing strategies.The establishment of Nash equilibrium of the game is proven,and a distributed artificial bee colonies algorithm is employed to achieve game equilibrium.Our proposed solution addresses these bottlenecks by leveraging a game-theoretic approach for task offloading and resource allocation in mobile edge computing(MEC)-enabled IoV environments.Simulation results demonstrate that the proposed scheme outperforms existing solutions in terms of convergence speed and system utility.Specifically,the total revenue achieved by our scheme surpasses other algorithms by at least 8.98%.

Game Theory Based Model for Predictive Analytics Using Distributed Position Function

This paper presents a game theory-based method for predicting the outcomes of negotiation and group decision-making problems. We propose an extension to the BDM model to address problems where actors’ positions are distributed over a position spectrum. We generalize the concept of position in the model to incorporate continuous positions for the actors, enabling them to have more flexibility in defining their targets. We explore different possible functions to study the role of the position function and discuss appropriate distance measures for computing the distance between the positions of actors. To validate the proposed extension, we demonstrate the trustworthiness of our model’s performance and interpretation by replicating the results based on data used in earlier studies.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

Purpose-In order to solve the problem of inaccurate calculation of index weights,subjectivity and uncertainty of index assessment in the risk assessment process,this study aims to propose a scientific and reasonable centralized traffic control(CTC)system risk assessment method.Design/methodologylapproach-First,system-theoretic process analysis(STPA)is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis.Then,to enhance the accuracy of weight calculation,the fuzzy analytical hierarchy process(FAHP),fuzzy decision-making trial and evaluation laboratory(FDEMATEL)and entropy weight method are employed to calculate the subjective weight,relative weight and objective weight of each index.These three types of weights are combined using game theory to obtain the combined weight for each index.To reduce subjectivity and uncertainty in the assessment process,the backward cloud generator method is utilized to obtain the numerical character(NC)of the cloud model for each index.The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system.This cloud model is used to obtain the CTC system's comprehensive risk assessment.The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud.Finally,this process yields the risk assessment results for the CTC system.Findings-The cloud model can handle the subjectivity and fuzziness in the risk assessment process well.The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/value-This study provides a cloud model-based method for risk assessment of CTC systems,which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment,achieving effective risk assessment of CTC systems.It can provide a reference and theoretical basis for risk management of the CTC system.

Game Theory Optimization via Diverse Genetic Crossover Intelligence

Game theory is explored via a maze application where combinatorial optimization occurs with the objective of traversing through a defined maze with an aim to enhance decision support and locate the optimal travel sequence while minimizing computation time. This combinatorial optimization approach is initially demonstrated by utilizing a traditional genetic algorithm (GA), followed by the incorporation of artificial intelligence utilizing embedded rules based on domain-specific knowledge. The aim of this initiative is to compare the results of the traditional and rule-based optimization approaches with results acquired through an intelligent crossover methodology. The intelligent crossover approach encompasses a two-dimensional GA encoding where a second chromosome string is introduced within the GA, offering a sophisticated means for chromosome crossover amongst selected parents. Additionally, parent selection intelligence is incorporated where the best-traversed paths or population members are retained and utilized as potential parents to mate with parents selected within a traditional GA methodology. A further enhancement regarding the utilization of saved optimal population members as potential parents is mathematically explored within this literature.

On Ethical Literary Criticism and Game Theory--An Interview with Professor Nie Zhenzhao

In 2014,Huang Kaihong,a professor at School of Foreign Languages and Cultures,Southwest University of Science and Technology,interviewed the Doctoral advisor Professor Nie Zhenzhao during the period of his academic visiting to Central China Normal University.As early as in 2005,Huang Kaihong conducted an interview with Professor Nie Zhenzhao on the topic of the general introduction of ethical literary criticism.So around 11 years later,the second interview mainly covers not only the ethical literary criticism theory,but the game theory and the relationship between them as well.Professor Nie thinks whether the game theory can be applied to literature research is still under discussion.The theory of ethical literary criticism is a kind of methodology based on science and it can get the attention of literary critics at home and abroad,which is because it fits the practical needs of literary criticism,draws the literary criticism away from only emphasizing criticism genres and the research of criticism terms,and pays attention to the true nature of the literary text in literature research.After consulting Professor Nie Zhenzhao about some related questions from the perspective of game theory.Huang Kaihong gets some significant information concerning literature research and understands the latest core terms and the concrete application method of ethical literary criticism,especially the relationship between the instructing and aesthetic functions of literature.

An evolutionary game theory-based machine learning framework for predicting mandatory lane change decision

Mandatory lane change(MLC)is likely to cause traffic oscillations,which have a negative impact on traffic efficiency and safety.There is a rapid increase in research on mandatory lane change decision(MLCD)prediction,which can be categorized into physics-based models and machine-learning models.Both types of models have their advantages and disadvantages.To obtain a more advanced MLCD prediction method,this study proposes a hybrid architecture,which combines the Evolutionary Game Theory(EGT)based model(considering data efficient and interpretable)and the Machine Learning(ML)based model(considering high prediction accuracy)to model the mandatory lane change decision of multi-style drivers(i.e.EGTML framework).Therefore,EGT is utilized to introduce physical information,which can describe the progressive cooperative interactions between drivers and predict the decision-making of multi-style drivers.The generalization of the EGTML method is further validated using four machine learning models:ANN,RF,LightGBM,and XGBoost.The superiority of EGTML is demonstrated using real-world data(i.e.,Next Generation SIMulation,NGSIM).The results of sensitivity analysis show that the EGTML model outperforms the general ML model,especially when the data is sparse.

Research on Children’s Game Activity Design Strategies Based on Embodied Cognition Theory

Edutainment,in the kindergarten education stage,emphasizes the game as the basic activity and combines the content of education with the form of the game,thus it also forms the educational method of gamification teaching.Through investigation and analysis,it is found that the current kindergarten game activity design has the problem of improper combination of educational content and game form.The current kindergarten game activity design has problems such as stereotypes,children’s lack of active learning opportunities in activities,teachers’insufficient theoretical understanding,inappropriate teacher guidance methods,and so on.Embodied cognition theory attaches importance to the important role of the body in the development of cognition,provides new guidance for classroom teaching,and opens up a new path for classroom teaching reform.Based on the perspective of embodied cognition theory,the concept of body and mind integration should be adhered to in kindergarten teaching with games as the basic activity,experiential teaching situation should be created,children’s subjective experience should be respected,and games and interactions should be designed to promote children’s physical and mental participation,thus laying a foundation for the realization of children’s individual freedom,autonomy,and all-round development.Therefore,this paper aims at the existing problems in the current kindergarten gamification teaching and discusses the design strategy of children’s game activities based on embodied cognition theory.

Evolutionary dynamics of tax-based strong altruistic reward andpunishment in a public goods game

In public goods games, punishments and rewards have been shown to be effective mechanisms for maintaining individualcooperation. However, punishments and rewards are costly to incentivize cooperation. Therefore, the generation ofcostly penalties and rewards has been a complex problem in promoting the development of cooperation. In real society,specialized institutions exist to punish evil people or reward good people by collecting taxes. We propose a strong altruisticpunishment or reward strategy in the public goods game through this phenomenon. Through theoretical analysis and numericalcalculation, we can get that tax-based strong altruistic punishment (reward) has more evolutionary advantages thantraditional strong altruistic punishment (reward) in maintaining cooperation and tax-based strong altruistic reward leads toa higher level of cooperation than tax-based strong altruistic punishment.

The Triple Helix of innovation as a double game involving domestic and foreign actors

Purpose:The collaboration relationships between innovation actors at a geographic level may be considered as grouping two separate layers,the domestic and the foreign.At the level of each layer,the relationships and the actors involved constitute a Triple Helix game.The paper distinguished three levels of analysis:the global grouping together all actors,the domestic grouping together domestic actors,and the foreign related to only actors from partner countries.Design/methodology/approach:Bibliographic records data from the Web of Science for South Korea and West Africa breakdown per innovation actors and distinguishing domestic and international collaboration are analyzed with game theory.The core,the Shapley value,and the nucleolus are computed at the three levels to measure the synergy between actors.Findings:The synergy operates more in South Korea than in West Africa;the government is more present in West Africa than in South Korea;domestic actors create more synergy in South Korea,but foreign more in West Africa;South Korea can consume all the foreign synergy,which is not the case of West Africa.Research limitations:Research data are limited to publication records;techniques and methods used may be extended to other research outputs.Practical implications:West African governments should increase their investment in science,technology,and innovation to benefit more from the synergy their innovation actors contributed at the foreign level.However,the results of the current study may not be sufficient to prove that greater investment will yield benefits from foreign synergies.Originality/value:This paper uses game theory to assess innovation systems by computing the contribution of foreign actors to knowledge production at an area level.It proposes an indicator to this end.

A Value for Games Defined on Graphs

Given a graph g=( V,A ) , we define a space of subgraphs M with the binary operation of union and the unique decomposition property into blocks. This space allows us to discuss a notion of minimal subgraphs (minimal coalitions) that are of interest for the game. Additionally, a partition of the game is defined in terms of the gain of each block, and subsequently, a solution to the game is defined based on distributing to each player (node and edge) present in each block a payment proportional to their contribution to the coalition.

Hybrid data-driven framework for shale gas production performance analysis via game theory, machine learning, and optimization approaches

A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.

Zero-determinant strategies in iterated multi-strategy games

Self-serving,rational agents sometimes cooperate to their mutual benefit.The two-player iterated prisoner′s dilemma game is a model for including the emergence of cooperation.It is generally believed that there is no simple ultimatum strategy which a player can control the return of the other participants.The zero-determinant strategy in the iterated prisoner′s dilemma dramatically expands our understanding of the classic game by uncovering strategies that provide a unilateral advantage to sentient players pitted against unwitting opponents.However,strategies in the prisoner′s dilemma game are only two strategies.Are there these results for general multi-strategy games?To address this question,the paper develops a theory for zero-determinant strategies for multi-strategy games,with any number of strategies.The analytical results exhibit a similar yet different scenario to the case of two-strategy games.The results are also applied to the Snowdrift game,the Hawk-Dove game and the Chicken game.

Solving the Olbers’s Paradox, Explaining the “Red-Shift”, and Challenging the Relativities by “Sun Matters Theory” and “Sun Model of Universe”, an Evolution of the Einstein’s Static Universe Model

Olbers’s paradox, known as the dark night paradox, is an argument in astrophysics that the darkness of the night sky conflicts with the assumption of an infinite and eternal static universe. Big-Bang theory was used to partially explain this paradox, while introducing new problems. Hereby, we propose a better theory, named Sun Matters Theory, to explain this paradox. Moreover, this unique theory supports and extended the Einstein’s static universe model proposed by Albert Einstein in 1917. Further, we proposed our new universe model, “Sun Model of Universe”. Based on the new model and novel theory, we generated innovative field equation by upgrading Einstein’s Field Equation through adding back the cosmological constant, introducing a new variable and modifying the gravitationally-related concepts. According to the Sun Model of Universe, the dark matter and dark energy comprise the so-called “Sun Matters”. The observed phenomenon like the red shift is explained as due to the interaction of ordinary light with Sun Matters leading to its energy and frequency decrease. In Sun Model, our big universe consists of many universes with ordinary matter at the core mixed and surrounded with the Sun Matters. In those universes, the laws of physics may be completely or partially different from that of our ordinary universe with parallel civilizations. The darkness of night can be easily explained as resulting from the interaction of light with the Sun Matters leading to the sharp decrease in the light intensity. Sun Matters also scatter the light from a star, which makes it shining as observed by Hubble. Further, there is a kind of Sun Matters named “Sun Waters”, surrounding every starts. When lights pass by the sun, the Sun Waters deflect the lights to bend the light path. According to the Sun Model, it is the light bent not the space bent that was proposed in the theory of relativities.

The “Dead Universe” Theory: Natural Separation of Galaxies Driven by the Remnants of a Supermassive Dead Universe

This article explores the dead universe theory as a novel interpretation for the origin and evolution of the universe, suggesting that our cosmos may have originated from the remnants of a preceding universe. This perspective challenges the conventional Big Bang theory, particularly concerning dark matter, the expansion of the universe, and the interpretation of phenomena such as gravitational waves.

The Axiology of Consciousness in Mocombeian Consciousness Field Theory

This work explores the axiology of consciousness in Mocombe’s consciousness field in the material world.The paper critically assesses Mocombe’s consciousness field theory(CFT)within the larger body of contemporary ontological debates regarding the nature,origin,and constitution of consciousness in the universe.The work goes on to highlight the manifestation of Mocombe’s consciousness field in the material resource framework that is the earth,and the nature and origins of ethics and values.

On Some Mathematical Connections between the Cyclic Universe, Inflationary Universe, p-Adic Inflation, p-Adic Cosmology and Various Sectors of Number Theory

This paper is a review, a thesis, of some interesting results that have been obtained in various research concerning the “brane collisions in string and M-theory” (Cyclic Universe), p-adic inflation and p-adic cosmology. In Section 2, we have described some equations concerning cosmic evolution in a Cyclic Universe. In Section 3, we have described some equations concerning the cosmological perturbations in a Big Crunch/Big Bang space-time, the M-theory model of a Big Crunch/Big Bang transition and some equations concerning the solution of a braneworld Big Crunch/Big Bang Cosmology. In Section 4, we have described some equations concerning the generating ekpyrotic curvature perturbations before the Big Bang, some equations concerning the effective five-dimensional theory of the strongly coupled heterotic string as a gauged version of N=1five-dimensional supergravity with four-dimensional boundaries, and some equations concerning the colliding branes and the origin of the Hot Big Bang. In Section 5, we have described some equations regarding the “null energy condition” violation concerning the inflationary models and some equations concerning the evolution to a smooth universe in an ekpyrotic contracting phase with w>1. In Section 6, we have described some equations concerning the approximate inflationary solutions rolling away from the unstable maximum of p-adic string theory. In Section 7, we have described various equations concerning the p-adic minisuperspace model, zeta strings, zeta nonlocal scalar fields and p-adic and adelic quantum cosmology. In Section 8, we have shown various and interesting mathematical connections between some equations concerning the p-adic inflation, the p-adic quantum cosmology, the zeta strings and the brane collisions in string and M-theory. Furthermore, in each section, we have shown the mathematical connections with various sectors of Number Theory, principally the Ramanujan’s modular equations, the Aurea Ratio and the Fibonacci’s numbers.

A label noise filtering and label missing supplement framework based on game theory

Labeled data is widely used in various classification tasks.However,there is a huge challenge that labels are often added artificially.Wrong labels added by malicious users will affect the training effect of the model.The unreliability of labeled data has hindered the research.In order to solve the above problems,we propose a framework of Label Noise Filtering and Missing Label Supplement(LNFS).And we take location labels in Location-Based Social Networks(LBSN)as an example to implement our framework.For the problem of label noise filtering,we first use FastText to transform the restaurant's labels into vectors,and then based on the assumption that the label most similar to all other labels in the location is most representative.We use cosine similarity to judge and select the most representative label.For the problem of label missing,we use simple common word similarity to judge the similarity of users'comments,and then use the label of the similar restaurant to supplement the missing labels.To optimize the performance of the model,we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the model.Finally,a case study is given to illustrate the effectiveness and reliability of LNFS.

Efficient Power Control for UAV Based on Trajectory and Game Theory

Due to the fact that network space is becoming more limited,the implementation of ultra-dense networks(UDNs)has the potential to enhance not only network coverage but also network throughput.Unmanned Aerial Vehicle(UAV)communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes.A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this article.In the IoT system,the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless nodes.The quality of service(QoS)offered by the cognitive node was interpreted as a price-based utility function,which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’net utility functions.An energyefficient non-cooperative game theory power allocation with pricing strategy abbreviated as(EE-NGPAP)is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things(IoT)wireless nodes.It has also been demonstrated,theoretically and by the use of simulations,that the Nash equilibrium does exist and that it is one of a kind.The proposed energy harvesting approach was shown,through simulations,to significantly reduce the typical amount of power thatwas sent.This is taken into consideration to agree with the objective of 5G networks.In order to converge to Nash Equilibrium(NE),the method that is advised only needs roughly 4 iterations,which makes it easier to utilise in the real world,where things aren’t always the same.